Proficient Use of Open Data Requires These Core Information Skills: An Open Data Community Perspective
Bibliographic record
Abstract
Expanding access to open data, such as government data and research data, requires that we consider how citizens and stakeholders can best access the value these data hold. Should individuals rely on an intermediary to create information products from the data, or should they dive in and work with raw data? Building on previous work defining a core set of data literacy skills, we convened a workshop with 34 open data professionals to define the core set of skills for working with open data: "open data literacy". Analysis of their perspectives reveals a focus on non-technical skills, like creativity, curiosity, and critical thinking, as a priority over technical skills like coding and visualization. We describe their perspective in detail, and reflect on the significance of our findings for information professionals.Élargir l'accès aux données ouvertes, telles que les données gouvernementales et les données de recherche, exige que nous examinions comment les citoyens et les parties prenantes peuvent le mieux accéder à la valeur de ces données. Les individus devraient-ils compter sur un intermédiaire pour créer des produits informationnels à partir des données, ou devraient-ils faire le plongeon et travailler avec les données brutes? Sur la base de travaux antérieurs définissant un ensemble de compétences de bases permettant de travailler avec des données, nous avons organisé un atelier avec 34 professionnels des données libres dans le but de définir un ensemble de compétences de base permettant de travailler avec des données ouvertes : « open data literacy ». L'analyse de leurs perspectives révèle que l'accent est mis sur les compétences non techniques, telles que la créativité, la curiosité et la pensée critique, plutôt que sur les compétences techniques comme le codage et la visualisation. Nous décrivons leur point de vue en détail et réfléchissons à l'importance de nos constatations pour les professionnels de l'information.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".